Several media services exist today that allow individuals to browse, sample, download and purchase various media files (e.g., individual songs, albums, movies and television shows) using their electronic devices (e.g., cellular telephones, personal digital assistants (PDAs), personal computers (PCs), laptops, etc.). Many of these services, which are often accessible via the Internet, may also provide recommendations services, whereby the service may monitor the user's activity and provide recommendations of media files that may be of interest to the user based on the user's apparent preferences. In particular, many of these services utilize a user account, including for example a username/user ID and password, to identify each user. When a user completes a transaction within or using the service, such as buying a music track or movie, the service may build a personal profile of the user. Based on the profile, the service may learn to provide personalized recommendations to the user. For example, the service may recommend media items or files (e.g., songs, albums, movies, television show episodes or series, etc.) that are similar to the items or files previously accessed, downloaded, purchased, or otherwised consumed, by the user.
One issue may arise, however, if someone other than the user downloads, or otherwise accesses, a media file or item while accessing the recommendation service under the user's account, which may contaminate the personalized recommendations. A need exists for an improved recommendation system that may overcome this and other challenges.